Edge AI development for embedded systems
Smarter embedded products without the cloud
We develop machine learning models that run directly on your device, whether it’s a microcontroller or GPU-class module, processing data locally at the point of capture. This enables decisions to happen faster, to lower failure points, and unlock hardware efficiency without increasing your bill of materials.
Processing at the device level
Traditional AI architectures depend heavily on the cloud, extracting raw data from a device, transmitting it over a network, and waiting for an external server to return an operational decision. Every step adds latency and a potential point of failure.
Edge AI changes this entirely. By embedding machine learning models directly into the device’s firmware, data is evaluated and acted on in real time, with no network dependency.
The Cloud
Data centre
Key benefits:
• Scales easily
• Lower device hardware costs
Network Edge
Gateway
Key benefits:
• Decreased latency
• Lower device hardware costs
Application Edge
Device/s
Edge AI operates here
Key benefits:
• Negligible latency
• Real-time ready
• Increased safety
• Increased security
We work across the full range of edge AI hardware
Edge AI isn’t one kind of hardware, and the right choice depends on the processing your product needs, the power and space it has, and where it runs. Bluefruit develops across the full range, matching the approach to what each product needs.
| Hardware class | Where it fits | What we build |
|---|---|---|
| TinyML | Low-power microcontrollers (MCUs) with tight memory budgets. | Sensorless diagnostics, anomaly detection, and always-on milliwatt sensing. |
| Embedded AI | Higher-end MCUs, NPUs, and dedicated accelerators. | On-device audio/vision processing, real-time classification, and hardware control. |
| Edge compute | Single-board computers (Raspberry Pi class) and gateway devices. | Heavier local inference, rapid prototyping, and network gateway roles. |
| GPU-class edge | NVIDIA Jetson modules and embedded high-performance GPUs. | Advanced computer vision, multi-stream deep learning, and robotics. |
Edge AI capabilities
Bluefruit combines embedded machine learning expertise with more than 25 years of embedded software engineering to deploy intelligent algorithms directly onto your target hardware.
Sensorless diagnostics
Design machine learning models that extract operational diagnostic and fault-reporting insights without the added unit cost, weight, or space of new physical components.
Acoustic and visual AI
Build lightweight, localised audio and image recognition algorithms for microcontrollers at the sensor level, capable of health monitoring, anomaly detection, and more.
Fluid and chemical composition
Develop models that analyse complex fluid composition in real time, checking ratios, concentrations, and properties without sending samples to external or cloud-based processing units.
Real-time biomarker tracking
Engineer edge-native algorithms capable of tracking biological changes and molecular binding, like hormone changes or antibody-antigen connections, on handheld or point-of-care devices.
Safety-critical systems
Embed machine learning into safety-critical and regulated systems to monitor performance and provide pre-emptive guidance to operators where human error carries high operational stakes.
24-hour manufacturing monitoring
Build robust models designed for continuous, round-the-clock monitoring of industrial manufacturing processes, like quality maintenance, fault reporting, and automated shutdown protocols.
Autonomous lifecycle management
Shift from reactive maintenance to predictive care. By embedding models that track device condition and system performance, you can anticipate failures and trigger parts ordering before downtime occurs.
Model training and data enhancement
Clean, label, and enhance real-world sensor datasets, then train and optimise machine learning models specifically to fit the strict memory and processing constraints of your target hardware.
Can edge AI be added to an existing product?
You don’t need to design a product from scratch to add edge AI. It can often be added through software alone, without new sensors or changes to the physical hardware. The approach relies on virtual sensors developed through machine learning models that extract condition, fault, or predictive information from data your device already produces. We’ve used this on existing product lines to add diagnostic capabilities without touching the bill of materials.
Edge AI in action
Backed by funding from AeroSpace Cornwall, Bluefruit Software’s Audio Classification Equipment (ACE) R&D project demonstrates what’s possible when deep learning convolutional neural networks run natively on standalone embedded hardware.
The system we built classifies live audio streams entirely at the edge, achieving over 90% accuracy on a standalone device without requiring a cloud connection. And we developed verification and validation practices specifically for convolutional neural networks, aligned with IEC 62304 and TIR45
Compliance-ready edge AI development
Deploying machine learning into regulated spaces like medical devices, aerospace systems, and critical industrial machinery demands strict regulatory compliance, with AI-specific scrutiny applied on top of existing standards.
We create audit trails, risk documentation, and verification that survives a certification review, and understand how important it is to build that compliance work into the development process from the start.
Regulated sector experience
We know how to engineer machine learning models that integrate seamlessly into complex, safety-critical system architectures where failure is not an option.
Standards and documentation
Our processes align with industry frameworks like IEC 62304 and AAMI TIR45, providing model traceability, data lineage, and risk assessments through automated living documentation.
Hardware validation
By testing on physical target hardware early in the process, we catch memory constraints, performance bottlenecks, and regressions before they affect your schedule.
❝ Paul and his team have worked with us on a number of projects and bring an extra dimension to software product development in terms of their commitment and technical expertise. ❞
❝ A 'can do' approach shines through on each project, with customer satisfaction very much at the top of the list. ❞
❝ With years of working together, we regard Bluefruit as a valued extension of our internal product development team. ❞
❝ Bluefruit provide a professional, innovative and technical team in a very friendly environment. They display a culture of continuous improvement in everything they do for us, this and their positive approach to every challenge makes them a great partner to work with. ❞
Getting capable models onto constrained hardware
We design and write optimised firmware without exhausting RAM, spiking latency, or draining device battery, using techniques like quantisation and pruning to make trained models work within the limits of your silicon ecosystem.
We’ve worked with industry-leading chips, including those developed by
Our technical approach to silicon ecosystems
Vendor framework integration
Working directly with manufacturer-specific AI toolkits – like STM32Cube AI Studio, TI Edge AI Studio, Simplicity Studio ML tools, and Edge Impulse integration – we bridge the gap between high-level machine learning frameworks and local hardware registers.
Firmware layer alignment
The C/C++ firmware we write maps your trained network’s inputs and outputs directly to your device’s physical hardware peripherals and driver layers.
Embedded runtime configuration
Lightweight, hardware-optimised embedded runtimes, configured and deployed to ensure the core execution engine fits cleanly within your microcontroller’s operating constraints.
Target hardware verification
Testing moves out of simulation environments early. Running on your chosen silicon target, we provide:
- On-device model validation: verifying the trained model produces accurate, reliable results on the target hardware under real-world conditions.
- Performance benchmarking: measuring key metrics such as latency, memory, throughput, and power consumption.
- Embedded AI validation: confirming the complete AI solution meets its functional, performance, and hardware requirements once embedded.
- Inference profiling: analysing model execution to identify bottlenecks, optimise runtime, and reduce memory and CPU usage.
- Deployment testing: validating the fully integrated AI application on target hardware for stability and resource use under real operating conditions.
Thinking about edge AI for your product?
Whether you’re exploring what edge AI could do for an existing product or looking to architect an upcoming project, we’re happy to talk through the options with you.
Frequently asked questions
How do we make embedded software development work? Through our processes, experience and skills.
Current and previous clients
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